STY: ruff-format reformat for frame.py

pandas/core/frame.py

@@ -1524,7 +1524,7 @@ def iterrows(self) -> Iterable[tuple[Hashable, Series]]:
         """
         columns = self.columns
         klass = self._constructor_sliced
-        for k, v in zip(self.index, self.values):
+        for k, v in zip(self.index, self.values, strict=True):
             s = klass(v, index=columns, name=k).__finalize__(self)
             if self._mgr.is_single_block:
                 s._mgr.add_references(self._mgr)
@@ -1607,10 +1607,10 @@ def itertuples(
             itertuple = collections.namedtuple(  # type: ignore[misc]
                 name, fields, rename=True
             )
-            return map(itertuple._make, zip(*arrays))
+            return map(itertuple._make, zip(*arrays, strict=True))
 
         # fallback to regular tuples
-        return zip(*arrays)
+        return zip(*arrays, strict=True)
 
     def __len__(self) -> int:
         """
@@ -4359,7 +4359,7 @@ def _setitem_array(self, key, value) -> None:
 
             if isinstance(value, DataFrame):
                 check_key_length(self.columns, key, value)
-                for k1, k2 in zip(key, value.columns):
+                for k1, k2 in zip(key, value.columns, strict=False):
                     self[k1] = value[k2]
 
             elif not is_list_like(value):
@@ -4465,7 +4465,7 @@ def _set_item_frame_value(self, key, value: DataFrame) -> None:
                 if len(cols_droplevel) and not cols_droplevel.equals(value.columns):
                     value = value.reindex(cols_droplevel, axis=1)
 
-                for col, col_droplevel in zip(cols, cols_droplevel):
+                for col, col_droplevel in zip(cols, cols_droplevel, strict=True):
                     self[col] = value[col_droplevel]
                 return
 
@@ -6567,7 +6567,11 @@ class    max    type
             names = self.index._get_default_index_names(names, default)
 
             if isinstance(self.index, MultiIndex):
-                to_insert = zip(reversed(self.index.levels), reversed(self.index.codes))
+                to_insert = zip(
+                    reversed(self.index.levels),
+                    reversed(self.index.codes),
+                    strict=True,
+                )
             else:
                 to_insert = ((self.index, None),)
 
@@ -7093,7 +7097,7 @@ def f(vals) -> tuple[np.ndarray, int]:
             result.name = None
         else:
             vals = (col.values for name, col in self.items() if name in subset)
-            labels, shape = map(list, zip(*map(f, vals)))
+            labels, shape = map(list, zip(*map(f, vals), strict=True))
 
             ids = get_group_index(labels, tuple(shape), sort=False, xnull=False)
             result = self._constructor_sliced(duplicated(ids, keep), index=self.index)
@@ -7346,7 +7350,9 @@ def sort_values(
 
             # need to rewrap columns in Series to apply key function
             if key is not None:
-                keys_data = [Series(k, name=name) for (k, name) in zip(keys, by)]
+                keys_data = [
+                    Series(k, name=name) for (k, name) in zip(keys, by, strict=True)
+                ]
             else:
                 # error: Argument 1 to "list" has incompatible type
                 # "Generator[ExtensionArray | ndarray[Any, Any], None, None]";
@@ -8208,7 +8214,7 @@ def _dispatch_frame_op(
 
             arrays = [
                 array_op(_left, _right)
-                for _left, _right in zip(self._iter_column_arrays(), right)
+                for _left, _right in zip(self._iter_column_arrays(), right, strict=True)
             ]
 
         elif isinstance(right, Series):
@@ -11745,7 +11751,7 @@ def c(x):
                 return nanops.nancorr(x[0], x[1], method=method)
 
             correl = self._constructor_sliced(
-                map(c, zip(left.values.T, right.values.T)),
+                map(c, zip(left.values.T, right.values.T, strict=True)),
                 index=left.columns,
                 copy=False,
             )